Synthetic Simulation as a Hypothesis-Building Instrument in National Capability Research
Power, prosperity, and resilience represent the most consequential and defining priorities for any nation. Yet the analytical challenge that underpins these outcomes is persistent: national capability is inherently systemic, multi-dimensional, and generational, while most available indicators are fragmented, backward-looking, or proxy-based. The ambition of the Global Institute for National Capability (GINC) is to address this gap by constructing a coherent National Capability Framework that can support decision-grade analysis across policy, investment, and institutional strategy.
A core methodological decision in the early development of this framework has been the deliberate use of synthetic (AI-generated) expert simulations. This choice is not intended to substitute for real-world expert judgment, nor to produce definitive measures or ratings. Rather, it is a structured approach to hypothesis generation and stress-testing at a stage where conceptual clarity, internal coherence, and methodological robustness matter more than empirical finality.
National capability frameworks embed assumptions, about what constitutes capability, how capabilities interact, and how they compound over time. Traditionally, these assumptions are often implicit, shaped incrementally through expert consultation or inherited indicator sets. GINC’s approach makes these assumptions explicit and contestable. By using synthetic simulations to emulate expert reasoning across domains, we can rapidly surface tensions, inconsistencies, and blind spots within the framework itself. The objective is not to achieve empirical precision at this stage, but to test whether the underlying concepts and structure of the framework hold up under scrutiny.
Synthetic simulation enables three distinct research functions. First, it allows systematic pressure-testing of the framework’s structure. By generating large volumes of internally consistent expert-like judgments, we can observe whether proposed capability groupings behave as expected, whether frontier-based measures are sensitive to meaningful variation, and whether aggregation techniques distort or preserve underlying signals. This helps inform our understanding of how the framework performs, before it is applied more broadly or taken forward into institutional settings.
Second, it accelerates the development of measurement instruments. Survey design, scoring rubrics, and the evaluation techniques used to assess multi-dimensional performance, such as frontier distance, dominance relationships, and other non-linear comparison methods, are notoriously difficult to refine through small-N expert pilots alone. Synthetic data allows rapid iteration: hypotheses can be tested, revised, and retested at scale before real experts are asked to invest time and credibility. This reduces noise in subsequent empirical phases and improves the quality of expert engagement when it matters most.
Third, the use of synthetic simulation itself is explicitly evaluated as part of the research. A key hypothesis underpinning this work is that well-designed synthetic simulations can approximate certain structural properties of expert judgment, such as relative weighting, consistency across domains, and sensitivity to frontier performance, even if they cannot replace lived expertise or contextual intuition. Comparing synthetic outputs with real expert responses in later phases allows GINC to evaluate where simulation aligns, where it diverges, and why.
Crucially, this methodology preserves epistemic humility. The synthetic simulations do not “validate” the framework; they challenge it. They provide a controlled environment in which ideas can fail cheaply, transparently, and informatively. In complex systems research, where real-world experimentation is costly and slow, this is a feature, not a flaw.
GINC’s use of synthetic simulation should therefore be understood as a methodological scaffold: a means of transforming implicit assumptions into explicit hypotheses, and of subjecting those hypotheses to structured attack. The ultimate test of the National Capability Framework will rest with real data, real experts, and real-world decision-making. Synthetic simulation is simply how that journey begins, rigorously and openly.
